Software Engineer - Model Development and Management, AI Platform

This position is listed on behalf of a partner company, which manages all applications and next steps. Our partner is looking for a Software Engineer 5 – Model Development and Management, AI Platform based in the United States.

As a Software Engineer 5, you will help build and evolve the platform infrastructure that accelerates machine learning development, experimentation, and deployment  will design and develop intuitive tools, interfaces, and frameworks that empower machine learning researchers, engineers, and data scientists to work more efficiently. Collaborating with applied researchers and cross-functional engineering teams, you will translate complex technical needs into reliable, scalable platform solutions.

Your work will span the full machine learning lifecycle, from data and feature management to model training, experiment tracking, and production deployment. You will engage directly with the ML community to identify challenges, gather feedback, and continuously improve the developer experience. This remote opportunity offers the chance to work with cutting-edge technologies, contribute to open-source initiatives, and help shape the future of large-scale AI and machine learning infrastructure.

• Platform Development and Engineering: Design, build, and maintain scalable platform solutions, including software development kits (SDKs), developer frameworks, internal tools, and user interfaces that streamline machine learning workflows for researchers, ML engineers, and data scientists.

• Machine Learning Lifecycle Enablement: Develop tools and services that accelerate model creation, evaluation, experimentation, and deployment. Support workflows spanning data and feature management, model training, experiment tracking, and production integration to improve reliability and development speed.

• Software Architecture and Design: Apply strong software engineering principles to create maintainable, efficient, and resilient distributed applications. Make thoughtful architectural decisions that support scalability, extensibility, and the evolving needs of diverse machine learning use cases.

• Cross-Functional Collaboration: Partner with applied researchers, data scientists, ML engineers, and other engineering teams to gather requirements, understand technical challenges, and deliver solutions aligned with real-world research and production needs.

• Developer Experience and User Engagement: Maintain a strong user-first mindset by engaging with the machine learning community, collecting feedback, identifying pain points, and translating user needs into meaningful platform enhancements.

• Workflow Orchestration and Infrastructure: Contribute to the development and integration of machine learning workflow orchestration, experiment tracking, and feature or data infrastructure. Help ensure that platform components work seamlessly across complex, large-scale ML environments.

• Innovation and Continuous Improvement: Explore emerging machine learning technologies, improve existing development processes, and contribute to open-source projects where appropriate. Help advance platform capabilities that enable teams to experiment faster and deploy models more reliably.

• Professional
  
  Experience:
  
  At least five years of full-time software engineering or machine learning experience, supported by a bachelor's and master's degree or a PhD. A strong track record of delivering production-quality software and solving complex engineering challenges is essential.

• Software Engineering Expertise: Excellent software design and development skills in Python, along with proficiency in at least one additional language such as Scala, Java, or C++. Demonstrated ability to design robust, maintainable applications and work effectively with distributed systems.

• Platform Engineering
  
  Experience:
  
  Proven experience developing platform solutions, SDKs, developer frameworks, or internal tools for technical users. The ability to understand developer needs and build intuitive, user-focused solutions for ML researchers, engineers, and data scientists is particularly important.

• Machine Learning Systems Knowledge: Experience working with or alongside modern, large-scale machine learning services. A solid understanding of the end-to-end ML lifecycle, including data and feature pipelines, model training, experiment tracking, evaluation, and production deployment, is required.

• Workflow and Infrastructure Familiarity: Familiarity with ML workflow orchestration,…